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There are a large number of target data and fewer outlier data in the problem of one-class classification. The sample reduction methods used for two-class or multi-class classification cannot be applied to this problem. This paper presents a novel method to reduce the scale of training set for one-class classification. This method only preserves the sample locating near the data distribution which...
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